Automated deep learning based on customer driven noise diagnostic assist

ABSTRACT

Methods and apparatus are provided for diagnosing a vehicle. In one embodiment, a method includes: initiating, by a processor, a recording of a noise by at least one microphone based on user selection data from a user of the vehicle; receiving, by the processor, audio signal data based on the recording; generating, by the processor, vector data based on the audio signal data; processing, by the processor, the vector data with at least one trained machine, by the processor, learning model to determine a classification of the noise; predicting, by the processor, an action to be taken based on the classification; and storing, by the processor, the audio signal data, the classification, and the action in a datastore.

TECHNICAL FIELD

The technical field generally relates to systems and methods fordiagnosing noise in a vehicle, more particularly to diagnosing noise ina vehicle using deep learning techniques

Many noises exist when driving a vehicle. Some noises are associatedwith the vehicle functions while other noises are ambient noisesassociated with the environment. In some instances, a noise can indicatea faulty vehicle component. Accordingly, it is desirable to providesystems and methods for identifying noises of a vehicle that allows fordiagnosis of a vehicle. Furthermore, other desirable features andcharacteristics of the present invention will become apparent from thesubsequent detailed description and the appended claims, taken inconjunction with the accompanying drawings and the foregoing technicalfield and background.

SUMMARY

Methods and apparatus are provided for diagnosing a vehicle. In oneembodiment, a method includes: initiating, by a processor, a recordingof a noise by at least one microphone based on user selection data froma user of the vehicle; receiving, by the processor, audio signal databased on the recording; generating, by the processor, vector data basedon the audio signal data; processing, by the processor, the vector datawith at least one trained machine, by the processor, learning model todetermine a classification of the noise; predicting, by the processor,an action to be taken based on the classification; and storing, by theprocessor, the audio signal data, the classification, and the action ina datastore.

In various embodiments, the processing the vector data includesprocessing the vector data with a first trained machine learning modelto determine a first classification associated with a vehicle component.

In various embodiments, the method includes processing the vector datafurther includes processing the vector data with a second trainedmachine learning model to determine a second classification associated,wherein the second trained machine learning model is based on thevehicle component.

In various embodiments, the method includes generating interface data todisplay a user selection icon, that when, selected by the user generatesthe user selection data.

In various embodiments, the method includes displaying the userselection icon on a display of an infotainment system of the vehicle.

In various embodiments, the method includes displaying the userselection icon on a display of a user device associated with thevehicle.

In various embodiments, the method includes collecting vehicle dataassociated with the classification and associating the vehicle data withthe classification as metadata.

In various embodiments, the microphone is disposed on the vehicle.

In various embodiments, the microphone is disposed on a user deviceassociated with the vehicle.

In various embodiments, the method includes generating notification datato be displayed based on at least one of the classification and theaction.

In another embodiment, a system includes: at least one microphoneassociated with the vehicle; and a control module configured to, by aprocessor, initiate a recording of a noise by the at least onemicrophone based on user selection data from a user of the vehicle;receive audio signal data based on the recording; generate vector databased on the audio signal data; process the vector data with at leastone trained machine, by the processor, learning model to determine aclassification of the noise; predict an action to be taken based on theclassification; and store the audio signal data, the classification, andthe action in a datastore.

In various embodiments, the control module processes the vector data byprocessing the vector data with a first trained machine learning modelto determine a first classification associated with a vehicle component.

In various embodiments, the control module processes the vector data byprocessing the vector data with a second trained machine learning modelto determine a second classification associated, wherein the secondtrained machine learning model is based on the vehicle component.

In various embodiments, the control module generates interface data todisplay a user selection icon, that when selected by the user, generatesthe user selection data.

In various embodiments, the user selection icon is displayed on adisplay of an infotainment system of the vehicle.

In various embodiments, the user selection icon is displayed on adisplay of a user device associated with the vehicle.

In various embodiments, the control module collects vehicle dataassociated with the classification and associates the vehicle data withthe classification as metadata.

In various embodiments, the microphone is disposed on the vehicle.

In various embodiments, the microphone is disposed on a user deviceassociated with the vehicle.

In various embodiments, the control module generates notification datato be displayed based on at least one of the classification and theaction.

DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a schematic diagram of a vehicle including diagnostic systemin accordance with various embodiments;

FIG. 2 is a schematic diagram of a vehicle including microphones inaccordance with various embodiments;

FIG. 3 is a dataflow diagram illustrating a diagnostic module, inaccordance with various embodiments; and

FIGS. 4 and 5 are flowcharts illustrating exemplary diagnostic methods,in accordance with various embodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. As used herein, the term module refersto any hardware, software, firmware, electronic control component,processing logic, and/or processor device, individually or in anycombination, including without limitation: application specificintegrated circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that executes one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical block components and various processingsteps. It should be appreciated that such block components may berealized by any number of hardware, software, and/or firmware componentsconfigured to perform the specified functions. For example, anembodiment of the present disclosure may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments of the present disclosure maybe practiced in conjunction with any number of systems, and that thesystems described herein is merely exemplary embodiments of the presentdisclosure.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, and other functionalaspects of the systems (and the individual operating components of thesystems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships or physicalconnections may be present in an embodiment of the present disclosure.

With reference to FIG. 1 , a diagnostic system shown generally at 100 isassociated with a vehicle 10 in accordance with various embodiments. Aswill be discussed in more detail below, the diagnostic system 100records and processes noise for use in diagnosing a problem via acustomer action through an app or button of an infotainment system, or atelematics system. As will be discussed in more detail below, therecordings are made by way of microphones of the vehicle and/ormicrophones of a smartphone or other personal device. As will further bediscussed in more detail below, the processing of the microphonerecordings is by way of deep learning techniques.

As depicted in FIG. 1 , the vehicle 10 generally includes a chassis 12,a body 14, front wheels 16, and rear wheels 18. The body 14 is arrangedon the chassis 12 and substantially encloses components of the vehicle10. The body 14 and the chassis 12 may jointly form a frame. The wheels16-18 are each rotationally coupled to the chassis 12 near a respectivecorner of the body 14. In certain embodiments, the vehicle 10 comprisesan automobile, such as a sedan, truck, bus, or any number of differenttypes of automobiles. It should be appreciated, however, that thediagnostic system 100 and/or methods described herein may be implementedin other types of vehicles, including, but not limited to, aircraft andwatercraft. Also in various embodiments, the terms “noise” and “sound”may be used synonymously, unless otherwise noted herein.

As shown, the vehicle 10 generally includes a propulsion system 20, atransmission system 22, a steering system 24, a brake system 26, asensor system 28, an actuator system 30, at least one controller 34, anda communication module 36. The propulsion system 20 may, in variousembodiments, include an internal combustion engine, an electric machinesuch as a traction motor, and/or a fuel cell propulsion system. Thetransmission system 22 is configured to transmit power from thepropulsion system 20 to the vehicle wheels 16-18 according to selectablespeed ratios. According to various embodiments, the transmission system22 may include a step-ratio automatic transmission, acontinuously-variable transmission, or other appropriate transmission.The brake system 26 is configured to provide braking torque to thevehicle wheels 16-18. The brake system 26 may, in various embodiments,include friction brakes, brake by wire, a regenerative braking systemsuch as an electric machine, and/or other appropriate braking systems.The steering system 24 influences a position of the of the vehiclewheels 16-18. While depicted as including a steering wheel forillustrative purposes, in some embodiments contemplated within the scopeof the present disclosure, the steering system 24 may not include asteering wheel.

The sensor system 28 includes one or more sensing devices 40 a-40 n thatsense observable conditions of the exterior environment and/or theinterior environment of the vehicle 10. The sensing devices 40 a-40 ncan include, but are not limited to, radars, lidars, global positioningsystems, optical cameras, thermal cameras, ultrasonic sensors, inertialmeasurement units, and/or other sensors.

In various embodiments, the sensing devices 40 a-40 n include one ormore microphones. FIG. 2 illustrates exemplary microphones 50 a-50 f andexemplary placement relative to the vehicle 10, in accordance withvarious embodiments. The microphones 50 a-50 f generate signalscorresponding to sensed sounds associated with the vehicle 10, as isappreciated by those skilled in the art. The microphones may be singleelement analog microphones and/or digital microphone arrays. Dependingupon placement relative to the vehicle 10, the microphones 50 a-50 f areconfigured to, upon request, sense sounds within a cabin of the vehicle10, sounds associate with components of the vehicle 10, and/or soundsassociated with an environment of the vehicle 10.

With reference back to FIG. 1 , the actuator system 30 includes one ormore actuator devices 42 a-42 n that control one or more vehiclefeatures such as, but not limited to, the propulsion system 20, thetransmission system 22, the steering system 24, and the brake system 26.In various embodiments, the vehicle features can further includeinterior and/or exterior vehicle features such as, but are not limitedto, doors, a trunk, and cabin features such as air, music, lighting,etc. (not numbered).

The controller 34 includes at least one processor 44 and a computerreadable storage device or media 46. The processor 44 can be any custommade or commercially available processor, a central processing unit(CPU), a graphics processing unit (GPU), an auxiliary processor amongseveral processors associated with the controller 34, a semiconductorbased microprocessor (in the form of a microchip or chip set), amacroprocessor, any combination thereof, or generally any device forexecuting instructions. The computer readable storage device or media 46may include volatile and nonvolatile storage in read-only memory (ROM),random-access memory (RAM), and keep-alive memory (KAM), for example.KAM is a persistent or non-volatile memory that may be used to storevarious operating variables while the processor 44 is powered down. Thecomputer-readable storage device or media 46 may be implemented usingany of a number of known memory devices such as PROMs (programmableread-only memory), EPROMs (electrically PROM), EEPROMs (electricallyerasable PROM), flash memory, or any other electric, magnetic, optical,or combination memory devices capable of storing data, some of whichrepresent executable instructions, used by the controller 34 incontrolling the vehicle 10.

The instructions may include one or more separate programs, each ofwhich comprises an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theprocessor 44, receive and process signals from the sensor system 28,perform logic, calculations, methods and/or algorithms for automaticallycontrolling the components of the vehicle 10, and generate controlsignals to the actuator system 30 to automatically control thecomponents of the vehicle 10 based on the logic, calculations, methods,and/or algorithms. Although only one controller 34 is shown in FIG. 1 ,embodiments of the vehicle 10 can include any number of controllers 34that communicate by communication messages over any suitablecommunication medium or a combination of communication mediums and thatcooperate to process the sensor signals, perform logic, calculations,methods, and/or algorithms, and generate control signals to controlfeatures of the vehicle 10.

In various embodiments, the vehicle 10 further includes an infotainmentsystem 52, and/or a telematics system 54. The infotainment system 52includes, for example, at least a memory and a processor and isconfigured to provide information (e.g., navigation, time, heading,ambient temperature, etc.) and/or entertainment (e.g., music, news,podcasts, videos, etc.) to occupants of the vehicle 10. The telematicssystem 54 includes, for example, at least a memory and a processor andis configured to provide communications between the vehicle 10 andremote entities, such as, but not limited to, a remote transportationsystem 56 (i.e., OnStar or other entity), and a user device 58 a desktopcomputer; a mobile computer (e.g., a tablet computer, a laptop computer,or a netbook computer); a smartphone; a video game device; a digitalmedia player; a piece of home entertainment equipment; a digital cameraor video camera; a wearable computing device (e.g., smart watch, smartglasses, smart clothing); or the like. The telematics system 54 isconfigured to wirelessly communicate information, using Wi-Fi,Bluetooth, or other protocol known in the art.

The remote transportation system 56 includes at least a database 60 forstoring data associated with the recordings from the vehicle 10. Theuser device 58 includes at least a microphone 62 capable of capturingsounds associated with the vehicle 10.

In various embodiments, the diagnostic system 100 captures and processesaudio signals from the microphones 50 a-50 f, 62. All or parts of thediagnostic system 100 can reside on the infotainment system 52, thetelematics system 54, the user device 58, and/or the remotetransportation system 56. For example, the diagnostic system 100 can beimplemented as part of a feature of the infotainment system 52, afeature of the telematics system 54, and/or an app or web page of theuser device 58, etc.

The diagnostic system 100 is configured to request recordings by themicrophones 50 a-50 f, 62 automatically, upon predetermined events(e.g., a request initiated by a user), at scheduled intervals, etc.through the telematics system 54, the infotainment system 52, and/or theuser device 58. The diagnostic system 100 processes the audio signalswith one or more deep learning algorithms to predict one or moreclassifications of the recorded sound. The processing can be performedon the infotainment system 52, the telematics system 54, and/or or theuser device 58. The diagnostic system 100 then stores the recordingswith their associated classifications in the database of the remotetransportation system 56 and/or of a datastore of the vehicle 10 forfurther analysis, classification, and enrichment of thedatabase/datastore.

Referring now to FIG. 3 , and with continued reference to FIG. 1 , adataflow diagram illustrates various embodiments of a diagnostic module101 that is a part of the diagnostic system 100 as a part of thetelematics system 54, the infotainment system 52 and/or the user device58. Various embodiments of the diagnostic module 101 according to thepresent disclosure may include any number of sub-modules. As can beappreciated, the sub-modules shown in FIG. 3 may be combined and/orfurther partitioned to similarly record and process vehicle relatedsounds for diagnostics purposes. Inputs to the diagnostic module 101 maybe received from the sensing devices 40 a-40 n, the microphones 50 a-50n, 62, the I/O devices of the infotainment system 52, the telematicssystem 54 and/or the user device 58, received from other control modules(not shown) of the vehicle 10, and/or determined by other sub-modules(not shown) of the control module 34. In various embodiments, thediagnostic module 101 includes an audio signal capture module 102, avector quantization module 104, a classification module 106, an actionprediction module 108, and a model datastore 110.

In various embodiments, the model datastore 110 stores one or moretrained machine learning models 112 for processing audio signals inorder to predict one or more classifications of the sound. As can beappreciated, the models 112 can be trained in a supervised or supervisedfashion. In various embodiments, a variety of machine learningtechniques may be employed, including, for example, multivariateregression, artificial neural networks (ANNs), random forestclassifiers, Bayes classifiers (e.g., naive Bayes), principal componentanalysis (PCA), support vector machines, linear discriminant analysis,clustering algorithms (e.g., KNN), and/or the like. In some embodiments,multiple machine learning models 112 are used (e.g., via ensemblelearning techniques). As can be appreciated, embodiments of the presentdisclosure are not limited to any one machine learning technique.

In various embodiments, the audio signal capture module 102 capturesaudio signals recorded by one or more of the microphones 50 a-50 f, 62.For example, the audio signal capture module 102 generates interfacedata 116 for displaying one or more record selection icons through aninterface displayed by the infotainment system 52 and/or the user device58. In response to a selection by a user of the record selection icons,the audio signal capture module 102 receives user selection data 118generated by the infotainment system 52, the telematics system 54,and/or the user device 58 and initiates recording of one or moreselected microphones 50 a-50 f, 62 of the vehicle 10 or the user device58 via one or more microphone control signals 120. In response, theaudio signal capture module 102 receives the audio signals 114 recordedby the initiated microphones 50 a-50 f, 62.

In another example, the audio signal capture module 102 receives usercommand data 122 (e.g., indicating a spoken request by a user toinitiate recording) generated by a microphone 50 a-50 f of vehicle 10and initiates recording of one or more selected microphones 50 a-50 f,62 of the vehicle 10 or the user device 58 via the microphone controlsignals 120. In response, the audios signal capture module 102 receivesaudio signals 114 recorded by the initiated microphones 50 a-50 f, 62

In another example, the audio signal capture module 102 automatesinitiation of the recording of one or more selected 50 a-50 f of vehicle10 via the microphone control signals 120. In various embodiments, theautomation can be based on a scheduled interval, or an occurrence of apredetermined event.

In various embodiments, the audio signal capture module 102 stores theaudio signals in a datastore 124 for further processing. The datastore124 can reside on the vehicle 10 and/or the database 60 of the remotetransportation system 56. In various embodiments, the audio signalcapture module 102 provides the captured audio signals 114 to othermodules for further processing.

The vector quantization module 104 receives the captured audio signals114. The vector quantization module 104 processes the audio signal 114with a machine learning model such as a neural network to quantify theaudio signal 114 into vector data 126. The machine learning modelselects a vector from a finite set of possible vectors to represent theaudio signals 114. In various embodiments, other quantization methodscan be used including, but not limited to, tree-structured vectorquantization, direct sum vector quantization, Cartesian product vectorquantization, lattice vector quantization, classified vectorquantization, feedback vector quantization, and fuzzy vectorquantization.

The classification module 106 receives the vector data 126. Theclassification module 106 retrieves one or more trained machine learningmodels 112 from the model datastore 110 and processes the vector data126 with the machine learning models to predict a classification of thesound. The classification module 106 generates classification data 128based thereon.

In various embodiments, the classification module 106 processes thevector data 126 with a first trained machine learning model to predict aprimary classification associated with, for example, a vehicle component(e.g., engine noise, transmission noise, brake noise, road noise, tirenoise, wind noise, rattle noise, etc.). In various embodiments, theclassification module 106 then, based on the primary classification,retrieves a second trained machine learning model 112 from the modeldatastore 110 and processes the vector data 126 with the second trainedmachine learning model 112 to predict a secondary classificationassociated with the primary classification (e.g., when the primaryclassification, the secondary classification can be engine knockingnoise, diesel atomizer, noise, fuel pump noise, etc.) The classificationmodule 106 generates the classification data 128 based on the primaryclassification and the secondary classification.

The action prediction module 108 receives as input the classificationdata 128. The action prediction module 108 generates notification data130 indicating an action to be taken based on the noise classification.For example, the action can indicate to service the engine fuel system,service an exhaust system component, or no service necessary as thenoise does not indicate a fault. The actions to be taken can bepredefined according to standard repair techniques in the industry. Thenotification data 130 can be displayed by an interface for viewing by auser. The notification data 130 can be sent to a technician for reviewand confirmation. The notification data 130 can be stored in thedatastore 124 along with the classification data 128 for furtherdiagnostic purposes.

Referring now to FIGS. 4-5 , and with continued reference to FIGS. 1-3 ,flowcharts illustrate methods 300. 400 that can be performed by thesystem 100 of FIGS. 1-3 in accordance with the present disclosure. Ascan be appreciated in light of the disclosure, the order of operationwithin the methods 300, 400 is not limited to the sequential executionas illustrated in FIGS. 4-5 but may be performed in one or more varyingorders as applicable and in accordance with the present disclosure. Invarious embodiments, the methods 300, 400 can be scheduled to run basedon one or more predetermined events, and/or can run continuously duringoperation of the vehicle 10.

As depicted in FIG. 4 , the method 300 may begin at 305. User selectiondata 118 is received to initiate recording of a noise at 310. The audiosignal data 114 is received, including the recorded noise, at 320. Thesignal information is quantified into one or more vectors at 330. Theone or more vectors are processed with the trained machine learningmodel to predict a first general classification of the noise at 340.Thereafter, the one or more vectors are processed with a second trainedmachine learning model to predict a secondary classification associatedwith the first classification at 350.

Thereafter, vehicle data 127 is collected, for example, from the vehiclesensing devices 40 a-40 n at 360. The vehicle data 127 is associatedwith the classification as metadata and evaluated to predict an eventand generate notification data 130 based thereon at 370. The eventprediction, classifications, and metadata are stored in the datastorefor 24 for further evaluation by a user or technician at 380.Thereafter, the method may end at 390

As depicted in FIG. 5 , the method may begin at 405. Labeled sound datais compiled at 410. The labeled sound data is processed with the machinelearning model to train the machine learning model at 420. The trainedmachine learning model is saved in the machine learning model datastorefor further processing at 430. Thereafter, the method may end at 440.

It will be appreciated that the disclosed methods, systems, and vehiclesmay vary from those depicted in the Figures and described herein. Forexample, the diagnostic system 100, the vehicle 10, and/or variouscomponents thereof may vary from that depicted in FIGS. 1-3 anddescribed in connection therewith. It will similarly be appreciated thatthe steps of the methods may differ from and/or be performed in adifferent order than, and/or may otherwise differ from, and/or beimplemented differently than, the illustrations in FIGS. 4-5 and/or thediscussions above in connection therewith.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Itshould be understood that various changes can be made in the functionand arrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

What is claimed is:
 1. A method of diagnosing a vehicle, the methodcomprising: initiating, by a processor, a recording of a noise by atleast one microphone based on user selection data from a user of thevehicle; receiving, by the processor, audio signal data based on therecording; generating, by the processor, vector data based on the audiosignal data; processing, by the processor, the vector data with at leastone trained machine, by the processor, learning model to determine aclassification of the noise; predicting, by the processor, an action tobe taken based on the classification; and storing, by the processor, theaudio signal data, the classification, and the action in a datastore. 2.The method of claim 1, wherein the processing the vector data comprisesprocessing the vector data with a first trained machine learning modelto determine a first classification associated with a vehicle component.3. The method of claim 2, wherein the processing the vector data furthercomprises processing the vector data with a second trained machinelearning model to determine a second classification associated, whereinthe second trained machine learning model is based on the vehiclecomponent.
 4. The method of claim 1, further comprising generatinginterface data to display a user selection icon, that when, selected bythe user generates the user selection data.
 5. The method of claim 4,further comprising displaying the user selection icon on a display of aninfotainment system of the vehicle.
 6. The method of claim 4, furthercomprising displaying the user selection icon on a display of a userdevice associated with the vehicle.
 7. The method of claim 1, furthercomprising collecting vehicle data associated with the classificationand associating the vehicle data with the classification as metadata. 8.The method of claim 1, wherein the at least one microphone is disposedon the vehicle.
 9. The method of claim 1, wherein the at least onemicrophone is disposed on a user device associated with the vehicle. 10.The method of claim 1, further comprising generating notification datato be displayed based on at least one of the classification and theaction.
 11. A system for diagnosing a vehicle, comprising: at least onemicrophone associated with the vehicle; and a control module configuredto, by a processor, initiate a recording of a noise by the at least onemicrophone based on user selection data from a user of the vehicle;receive audio signal data based on the recording; generate vector databased on the audio signal data; process the vector data with at leastone trained machine, by the processor, learning model to determine aclassification of the noise; predict an action to be taken based on theclassification; and store the audio signal data, the classification, andthe action in a datastore.
 12. The system of claim 11, wherein thecontrol module processes the vector data by processing the vector datawith a first trained machine learning model to determine a firstclassification associated with a vehicle component.
 13. The system ofclaim 12, wherein the control module processes the vector data byprocessing the vector data with a second trained machine learning modelto determine a second classification associated, wherein the secondtrained machine learning model is based on the vehicle component. 14.The system of claim 11, wherein the control module generates interfacedata to display a user selection icon, that when selected by the user,generates the user selection data.
 15. The system of claim 14, whereinthe user selection icon is displayed on a display of an infotainmentsystem of the vehicle.
 16. The system of claim 14, wherein the userselection icon is displayed on a display of a user device associatedwith the vehicle.
 17. The system of claim 11, wherein the control modulecollects vehicle data associated with the classification and associatesthe vehicle data with the classification as metadata.
 18. The system ofclaim 11, wherein the at least one microphone is disposed on thevehicle.
 19. The system of claim 11, wherein the at least one microphoneis disposed on a user device associated with the vehicle.
 20. The systemof claim 11, wherein the control module generates notification data tobe displayed based on at least one of the classification and the action.